Characterizing the Spatial Patterns of Global Fertilizer Application and Manure Production

Potter, Philip; Ramankutty, Navin; Bennett, Elena M.; Donner, Simon D.

doi:10.1175/2010ei288.1

Cited by 116 publications

(194 citation statements)

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“…Our simulated increase in average yield from using ISFM was below the 739 kg/ha reported in Adimassu, Langan, Johnston, Mekuria, and Amede (2017); however, these authors' results are derived from a mixture of sources, including agronomic field trials, where manure was often applied in quantities that exceeded available farm manure resources. The quantity of manure in our ISFM technology aligned with manure production from reported livestock densities (Potter et al, 2010), but in the field trials manure quantities were generally unrelated to livestock densities, because they were experiments. For the effect of climate change on national yields, we found modest increases in maize yields and modest falls in wheat yields.…”

Section: Discussionmentioning

confidence: 93%

“…We designed crop management in DSSAT to reflect local contexts, subject to data availability. For example, we used zone-scale manure application rates, as observed in Potter et al (2010). Manure rates (kg nitrogen/ha) in the CSA technologies for maize were 31 (Zone 1), 16 (Zone 2), 10 (Zone 3), 34 (Zone 4), and 58 (Zone 5); and for wheat were 30 (Zone 1), 22 (Zone 2), 12 (Zone 3), 39 (Zone 4), and 58 (Zone 5).…”

Section: Simulating Crop Yieldsmentioning

confidence: 99%

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Economywide effects of climate‐smart agriculture in Ethiopia

Komarek

Thurlow

Koo

et al. 2019

Agricultural Economics

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Climate‐smart agriculture (CSA) is an approach for transforming and reorienting agricultural systems to support food security under climate change. Few studies, however, quantify at the national scale CSA's economic effects or compare CSA to input‐intensive technologies, like fertilizer or irrigation. Such quantification may help with priority setting among competing agricultural investment options. Our study uses an integrated biophysical and economic modeling approach to quantify and contrast the economywide effects of CSA (integrated soil fertility management in our study) and input‐intensive technologies in Ethiopia's cereal systems. We simulate impacts for 20‐year sequences of variable weather, with and without climate change. Results indicate that adopting CSA on 25% of Ethiopia's maize and wheat land increases annual gross domestic product (GDP) by an average 0.18% (US$49.8 million) and reduces the national poverty rate by 0.15 percentage points (112,100 people). CSA is more effective than doubling fertilizer use on the same area, which increases GDP by US$33.0 million and assists 75,300 people out of poverty. CSA and fertilizer have some substitutability, but CSA and irrigation appear complementary. Although not a panacea for food security concerns, greater adoption of CSA in Ethiopia could deliver economic gains but would need substantial tailoring to farmer‐specific contexts.

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Section: Discussionmentioning

confidence: 93%

Section: Simulating Crop Yieldsmentioning

confidence: 99%

Economywide effects of climate‐smart agriculture in Ethiopia

Komarek

Thurlow

Koo

et al. 2019

Agricultural Economics

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“…The pressures and corresponding characterization factors are summarized here in Table 3. We used the EE MRIO database Eora as basis for the calculations21, with added data for phosphorus and nitrogen5556575859.…”

Section: Methodsmentioning

confidence: 99%

Resource footprints and their ecosystem consequences

Verones

Moran

Stadler

et al. 2017

Sci Rep

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A meaningful environmental impact analysis should go beyond the accounting of pressures from resource use and actually assess how resource demand affects ecosystems. The various currently available footprints of nations report the environmental pressures e.g. water use or pollutant emissions, driven by consumption. However, there have been limited attempts to assess the environmental consequences of these pressures. Ultimately, consequences, not pressures, should guide environmental policymaking. The newly released LC-Impact method demonstrates progress on the path to providing this missing link. Here we present “ecosystem impact footprints” in terms of the consequences for biodiversity and assess the differences in impact footprint results from MRIO-based pressure footprints. The new perspective reveals major changes in the relative contribution of nations to global footprints. Wealthy countries have high pressure footprints in lower-income countries but their impact footprints often have their origin in higher-income countries. This shift in perspective provides a different insight on where to focus policy responses to preserve biodiversity.

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“…System-level metrics refer to the relationship between land management and properties of the socio-ecological system as a whole, such as the percentage of human appropriation of NPP (HANPP; Haberl et al 2007), and can provide a general idea of the overall management intensity. (Channan et al 2014) and (c) land-use intensity (LUI) split into high (shown in purple), medium (blue), low (green) and no use (white) levels for the following datasets: human appropriation of net primary productivity (HANPP; Haberl et al 2007), fertiliser inputs (Potter et al 2010), cereal yield (Monfreda et al 2008) and areas equipped for irrigation (Siebert et al 2005). Due to many 100% values, areas of high intensity are larger than other areas.…”

Section: Biome and Land-use Datamentioning

confidence: 99%

Agriculture rivals biomes in predicting global species richness

et al. 2016

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Species–area relationships (SARs) provide an avenue to model patterns of species richness and have recently been shown to vary substantially across regions of different climate, vegetation, and land cover. Given that a large proportion of the globe has been converted to agriculture, and considering the large variety in agricultural management practices, a key question is whether global SARs vary across gradients of agricultural intensity. We developed SARs for mammals that account for geographic variation in biomes, land cover and a range of land‐use intensity indicators representing inputs (e.g. fertilizer, irrigation), outputs (e.g. yields) and system‐level measures of intensity (e.g. human appropriation of net primary productivity – HANPP). We systematically compared the resulting SARs in terms of their predictive ability. Our global SAR with a universal slope was significantly improved by the inclusion of any one of the three variable types: biomes, land cover, and land‐use intensity. The latter, in the form of human appropriation of net primary productivity (HANPP), performed as well as biomes and land‐cover in predicting species richness. Other land‐use intensity indicators had a lower predictive ability. Our main finding that land‐use intensity performs as well as biomes and land cover in predicting species richness emphasizes that human factors are on a par with environmental factors in predicting global patterns of biodiversity. While our broad‐scale study cannot establish causality, human activity is known to drive species richness at a local scale, and our findings suggest that this may hold true at a global scale. The ability of land‐use intensity to explain variation in SARs at a global scale had not previously been assessed. Our study suggests that the inclusion of land‐use intensity in SAR models allows us to better predict and understand species richness patterns.

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Characterizing the Spatial Patterns of Global Fertilizer Application and Manure Production

Cited by 116 publications

References 0 publications

Economywide effects of climate‐smart agriculture in Ethiopia

Economywide effects of climate‐smart agriculture in Ethiopia

Resource footprints and their ecosystem consequences

Agriculture rivals biomes in predicting global species richness

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